首页|Studies from Western New England University Yield New Data on Machine Learning [Bidirectional Long Short-term Memory (Bilstm) - Support Vector Machine: a New Ma chine Learning Model for Predicting Water Quality Parameters]
Studies from Western New England University Yield New Data on Machine Learning [Bidirectional Long Short-term Memory (Bilstm) - Support Vector Machine: a New Ma chine Learning Model for Predicting Water Quality Parameters]
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in Machine Learning. According to news reporting from Springfield, Massachusetts, b y NewsRx journalists, research stated, "Water pollution threatens human health, agriculture, and ecosystems. Accurate prediction of water quality pa-rameters is crucial for effective protection." The news correspondents obtained a quote from the research from Western New Engl and University, "We suggest a novel hybrid deep learning model that enhances the efficiency of Support Vector Machines (SVMs) in predicting Electrical Conductiv ity (EC) and Total Dissolved Solids (TDS). Our model combines Bidirectional Long Short-Term Memory (BILSTM) and SVMs to extract essential features and predict o utput variables. We evaluated the models using input parameters (PH, Ca++, Mg++, Na+, K+, HCO3, SO4, and Cl) for one, two, and three-day predictions. Employing the Ali Baba and Forty Thieves (AFT) optimization algorithm, we identified optim al input combinations. The BILSTM-SVM model accurately estimated TDS values, wit h MAPE values of 2%, outperforming other models. Similarly, it succ essfully predicted EC values, exhibiting an R2 value of 0.94."
SpringfieldMassachusettsUnited State sNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningSupport Vector MachinesVector MachinesWestern New England University
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